Stacks API Lab / Collection
Design AI features around the whole application: inputs, retrieval, model calls, evaluation, and operational behavior. Begin with an observable user task and a clear boundary for each component.
Design AI features around the whole application: inputs, retrieval, model calls, evaluation, and operational behavior. Begin with an observable user task and a clear boundary for each component.
A useful AI design begins with the user action and the information available to support it. Separate the source of context from the component that reasons over it. Decide what may be logged, what may be sent outside the system, and which actions require another check.
Use the architecture guide to list the boundaries in a representative feature. Choose examples that reveal both useful behavior and realistic mistakes. When comparing designs, keep the task and review method consistent so that changes are meaningful. Include a recovery path when the model or a dependency cannot complete the work.
Continue with the AI Stacks API guide, or explore the complete stack atlas to connect this topic with the rest of the system.
An LLM feature needs more than a model choice. Map the application, data, evaluation, and operating responsibilities first, then compare architectures using representative tasks and measured outcomes instead of unsupported performance promises.
Read the guide : Design an AI and LLM Stack Around Boundaries and Evidence